Underwater target detection with hyperspectral remote-sensing imagery

This paper presents a new way of detecting underwater targets with hyperspectral remote-sensing data. The idea is to use a bathymetric model of subsurface reflectance to correct the spectral distortions due to water crossing. Then we derive the Matched filter (MF) from the Likelihood Ratio Test (LRT) built to decide whether the target is present or absent. Tested on both simulated and real images, this new detector appears to overcome classical filters in case of underwater targets. If the depth is unknown, it can be estimated using the maximum likelihood approach, and we show on simulations that detection performances are not very sensitive to the depth estimation accuracy.